High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

LCM-MVSNet		99.95 1	99.95 1	99.95 1	99.99 1	99.99 1	99.95 2	99.97 2	99.99 1	100.00 1	99.98 8	99.78 6	100.00 1	99.92 1	100.00 1	99.87 10

mvs_tets		99.90 2	99.90 2	99.90 5	99.96 4	99.79 35	99.72 20	99.88 16	99.92 7	99.98 4	99.93 14	99.94 1	99.98 6	99.77 12	100.00 1	99.92 3

test_part1		99.89 3	99.88 4	99.94 2	99.91 15	99.92 2	99.92 3	99.90 11	99.98 2	99.99 3	99.97 9	99.50 21	99.98 6	99.73 16	100.00 1	99.92 3

jajsoiax		99.89 3	99.89 3	99.89 8	99.96 4	99.78 38	99.70 23	99.86 20	99.89 12	99.98 4	99.90 22	99.94 1	99.98 6	99.75 13	100.00 1	99.90 5

ANet_high		99.88 5	99.87 5	99.91 3	99.99 1	99.91 3	99.65 44	100.00 1	99.90 8	100.00 1	99.97 9	99.61 16	99.97 17	99.75 13	100.00 1	99.84 15

LTVRE_ROB	99.19 1	99.88 5	99.87 5	99.88 12	99.91 15	99.90 5	99.96 1	99.92 5	99.90 8	99.97 7	99.87 31	99.81 5	99.95 43	99.54 25	99.99 13	99.80 24
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
pmmvs6		99.86 7	99.86 7	99.83 22	99.94 10	99.90 5	99.83 7	99.91 8	99.85 20	99.94 12	99.95 12	99.73 8	99.90 123	99.65 17	99.97 30	99.69 51

UniMVSNet_ETH3D		99.85 8	99.83 8	99.90 5	99.89 22	99.91 3	99.89 5	99.71 91	99.93 5	99.95 11	99.89 26	99.71 9	99.96 34	99.51 29	99.97 30	99.84 15

PS-MVSNAJss		99.84 9	99.82 9	99.89 8	99.96 4	99.77 40	99.68 32	99.85 24	99.95 4	99.98 4	99.92 17	99.28 41	99.98 6	99.75 13	100.00 1	99.94 2

test_djsdf		99.84 9	99.81 10	99.91 3	99.94 10	99.84 17	99.77 12	99.80 47	99.73 37	99.97 7	99.92 17	99.77 7	99.98 6	99.43 36	100.00 1	99.90 5

v7n		99.82 11	99.80 11	99.88 12	99.96 4	99.84 17	99.82 9	99.82 37	99.84 22	99.94 12	99.91 20	99.13 58	99.96 34	99.83 9	99.99 13	99.83 19

anonymousdsp		99.80 12	99.77 13	99.90 5	99.96 4	99.88 7	99.73 17	99.85 24	99.70 44	99.92 19	99.93 14	99.45 22	99.97 17	99.36 45	100.00 1	99.85 14

pm-mvs1		99.79 13	99.79 12	99.78 38	99.91 15	99.83 21	99.76 14	99.87 18	99.73 37	99.89 27	99.87 31	99.63 14	99.87 164	99.54 25	99.92 73	99.63 93

UA-Net		99.78 14	99.76 15	99.86 17	99.72 107	99.71 63	99.91 4	99.95 4	99.96 3	99.71 97	99.91 20	99.15 53	99.97 17	99.50 31	100.00 1	99.90 5

TransMVSNet (Re)		99.78 14	99.77 13	99.81 27	99.91 15	99.85 12	99.75 15	99.86 20	99.70 44	99.91 21	99.89 26	99.60 18	99.87 164	99.59 20	99.74 181	99.71 45

OurMVSNet-221017-0		99.75 16	99.71 17	99.84 20	99.96 4	99.83 21	99.83 7	99.85 24	99.80 30	99.93 15	99.93 14	98.54 134	99.93 67	99.59 20	99.98 22	99.76 36

Vis-MVSNet		99.75 16	99.74 16	99.79 35	99.88 24	99.66 81	99.69 29	99.92 5	99.67 51	99.77 72	99.75 79	99.61 16	99.98 6	99.35 46	99.98 22	99.72 42
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TDRefinement		99.72 18	99.70 18	99.77 40	99.90 20	99.85 12	99.86 6	99.92 5	99.69 47	99.78 67	99.92 17	99.37 30	99.88 151	98.93 105	99.95 48	99.60 116

XXY-MVS		99.71 19	99.67 21	99.81 27	99.89 22	99.72 61	99.59 56	99.82 37	99.39 103	99.82 49	99.84 40	99.38 28	99.91 103	99.38 42	99.93 69	99.80 24

nrg030		99.70 20	99.66 22	99.82 24	99.76 84	99.84 17	99.61 51	99.70 95	99.93 5	99.78 67	99.68 123	99.10 59	99.78 267	99.45 34	99.96 41	99.83 19

FC-MVSNet-test		99.70 20	99.65 23	99.86 17	99.88 24	99.86 11	99.72 20	99.78 57	99.90 8	99.82 49	99.83 41	98.45 149	99.87 164	99.51 29	99.97 30	99.86 12

v10		99.69 22	99.69 19	99.66 93	99.81 50	99.39 148	99.66 39	99.75 71	99.60 73	99.92 19	99.87 31	98.75 108	99.86 184	99.90 2	99.99 13	99.73 41

v8		99.68 23	99.69 19	99.65 98	99.80 56	99.40 146	99.66 39	99.76 65	99.64 59	99.93 15	99.85 36	98.66 119	99.84 218	99.88 6	99.99 13	99.71 45

DTE-MVSNet		99.68 23	99.61 30	99.88 12	99.80 56	99.87 8	99.67 36	99.71 91	99.72 40	99.84 42	99.78 65	98.67 117	99.97 17	99.30 55	99.95 48	99.80 24

VPA-MVSNet		99.66 25	99.62 26	99.79 35	99.68 128	99.75 48	99.62 47	99.69 101	99.85 20	99.80 59	99.81 50	98.81 93	99.91 103	99.47 33	99.88 99	99.70 48

PS-CasMVS		99.66 25	99.58 35	99.89 8	99.80 56	99.85 12	99.66 39	99.73 79	99.62 63	99.84 42	99.71 99	98.62 123	99.96 34	99.30 55	99.96 41	99.86 12

PEN-MVS		99.66 25	99.59 33	99.89 8	99.83 37	99.87 8	99.66 39	99.73 79	99.70 44	99.84 42	99.73 86	98.56 131	99.96 34	99.29 58	99.94 61	99.83 19

FMVSNet1		99.66 25	99.63 25	99.73 68	99.78 72	99.77 40	99.68 32	99.70 95	99.67 51	99.82 49	99.83 41	98.98 74	99.90 123	99.24 62	99.97 30	99.53 154

MIMVSNet1		99.66 25	99.62 26	99.80 30	99.94 10	99.87 8	99.69 29	99.77 60	99.78 33	99.93 15	99.89 26	97.94 195	99.92 85	99.65 17	99.98 22	99.62 105

FIs		99.65 30	99.58 35	99.84 20	99.84 34	99.85 12	99.66 39	99.75 71	99.86 17	99.74 87	99.79 58	98.27 168	99.85 202	99.37 44	99.93 69	99.83 19

casdiffmvs		99.63 31	99.61 30	99.67 86	99.79 66	99.59 105	99.13 162	99.85 24	99.79 32	99.76 74	99.72 92	99.33 35	99.82 239	99.21 63	99.94 61	99.59 125

baseline		99.63 31	99.62 26	99.66 93	99.80 56	99.62 94	99.44 75	99.80 47	99.71 41	99.72 92	99.69 112	99.15 53	99.83 229	99.32 51	99.94 61	99.53 154

Anonymous20231211		99.62 33	99.57 38	99.76 46	99.61 144	99.60 102	99.81 10	99.73 79	99.82 26	99.90 23	99.90 22	97.97 194	99.86 184	99.42 40	99.96 41	99.80 24

DeepC-MVS	98.90 4	99.62 33	99.61 30	99.67 86	99.72 107	99.44 133	99.24 124	99.71 91	99.27 118	99.93 15	99.90 22	99.70 11	99.93 67	98.99 93	99.99 13	99.64 88
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
WR-MVS_H		99.61 35	99.53 48	99.87 15	99.80 56	99.83 21	99.67 36	99.75 71	99.58 76	99.85 39	99.69 112	98.18 179	99.94 54	99.28 60	99.95 48	99.83 19

ACMH	98.42 6	99.59 36	99.54 44	99.72 74	99.86 30	99.62 94	99.56 61	99.79 53	98.77 189	99.80 59	99.85 36	99.64 13	99.85 202	98.70 122	99.89 91	99.70 48
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
testing_2		99.58 37	99.56 42	99.62 118	99.81 50	99.44 133	99.14 155	99.43 231	99.69 47	99.82 49	99.79 58	99.14 55	99.79 263	99.31 54	99.95 48	99.63 93

v1192		99.57 38	99.57 38	99.57 135	99.77 80	99.22 189	99.04 181	99.60 152	99.18 132	99.87 37	99.72 92	99.08 64	99.85 202	99.89 5	99.98 22	99.66 74

EG-PatchMatch MVS		99.57 38	99.56 42	99.62 118	99.77 80	99.33 164	99.26 116	99.76 65	99.32 112	99.80 59	99.78 65	99.29 39	99.87 164	99.15 77	99.91 82	99.66 74

Gipuma		99.57 38	99.59 33	99.49 158	99.98 3	99.71 63	99.72 20	99.84 30	99.81 27	99.94 12	99.78 65	98.91 83	99.71 291	98.41 135	99.95 48	99.05 284
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
v1921920		99.56 41	99.57 38	99.55 142	99.75 94	99.11 203	99.05 179	99.61 141	99.15 141	99.88 33	99.71 99	99.08 64	99.87 164	99.90 2	99.97 30	99.66 74

v1240		99.56 41	99.58 35	99.51 152	99.80 56	99.00 214	99.00 188	99.65 123	99.15 141	99.90 23	99.75 79	99.09 61	99.88 151	99.90 2	99.96 41	99.67 64

V42		99.56 41	99.54 44	99.63 109	99.79 66	99.46 126	99.39 82	99.59 159	99.24 124	99.86 38	99.70 106	98.55 132	99.82 239	99.79 11	99.95 48	99.60 116

v144192		99.55 44	99.54 44	99.58 130	99.78 72	99.20 195	99.11 168	99.62 134	99.18 132	99.89 27	99.72 92	98.66 119	99.87 164	99.88 6	99.97 30	99.66 74

test20.03		99.55 44	99.54 44	99.58 130	99.79 66	99.37 154	99.02 184	99.89 13	99.60 73	99.82 49	99.62 159	98.81 93	99.89 136	99.43 36	99.86 114	99.47 186

v1144		99.54 46	99.53 48	99.59 126	99.79 66	99.28 172	99.10 169	99.61 141	99.20 130	99.84 42	99.73 86	98.67 117	99.84 218	99.86 8	99.98 22	99.64 88

CP-MVSNet		99.54 46	99.43 61	99.87 15	99.76 84	99.82 25	99.57 59	99.61 141	99.54 77	99.80 59	99.64 140	97.79 208	99.95 43	99.21 63	99.94 61	99.84 15

TranMVSNet+NR-MVSNet		99.54 46	99.47 52	99.76 46	99.58 151	99.64 88	99.30 104	99.63 131	99.61 67	99.71 97	99.56 194	98.76 106	99.96 34	99.14 83	99.92 73	99.68 57

v2v482		99.50 49	99.47 52	99.58 130	99.78 72	99.25 180	99.14 155	99.58 168	99.25 122	99.81 56	99.62 159	98.24 170	99.84 218	99.83 9	99.97 30	99.64 88

ACMH+	98.40 8	99.50 49	99.43 61	99.71 78	99.86 30	99.76 46	99.32 97	99.77 60	99.53 79	99.77 72	99.76 75	99.26 45	99.78 267	97.77 192	99.88 99	99.60 116

Baseline_NR-MVSNet		99.49 51	99.37 70	99.82 24	99.91 15	99.84 17	98.83 215	99.86 20	99.68 49	99.65 116	99.88 29	97.67 216	99.87 164	99.03 90	99.86 114	99.76 36

TAMVS		99.49 51	99.45 56	99.63 109	99.48 205	99.42 141	99.45 72	99.57 170	99.66 55	99.78 67	99.83 41	97.85 204	99.86 184	99.44 35	99.96 41	99.61 112

pmmvs-eth3d		99.48 53	99.47 52	99.51 152	99.77 80	99.41 145	98.81 220	99.66 112	99.42 102	99.75 79	99.66 133	99.20 48	99.76 277	98.98 95	99.99 13	99.36 220

EI-MVSNet-UG-set		99.48 53	99.50 50	99.42 177	99.57 161	98.65 244	99.24 124	99.46 223	99.68 49	99.80 59	99.66 133	98.99 73	99.89 136	99.19 68	99.90 83	99.72 42

APDe-MVS		99.48 53	99.36 73	99.85 19	99.55 172	99.81 28	99.50 65	99.69 101	98.99 158	99.75 79	99.71 99	98.79 100	99.93 67	98.46 134	99.85 117	99.80 24

PMMVS2		99.48 53	99.45 56	99.57 135	99.76 84	98.99 215	98.09 288	99.90 11	98.95 164	99.78 67	99.58 183	99.57 19	99.93 67	99.48 32	99.95 48	99.79 30

DSMNet-mixed		99.48 53	99.65 23	98.95 252	99.71 110	97.27 302	99.50 65	99.82 37	99.59 75	99.41 194	99.85 36	99.62 15	100.00 1	99.53 27	99.89 91	99.59 125

DP-MVS		99.48 53	99.39 65	99.74 60	99.57 161	99.62 94	99.29 111	99.61 141	99.87 15	99.74 87	99.76 75	98.69 113	99.87 164	98.20 154	99.80 153	99.75 39

EI-MVSNet-Vis-set		99.47 59	99.49 51	99.42 177	99.57 161	98.66 242	99.24 124	99.46 223	99.67 51	99.79 64	99.65 138	98.97 76	99.89 136	99.15 77	99.89 91	99.71 45

VPNet		99.46 60	99.37 70	99.71 78	99.82 43	99.59 105	99.48 69	99.70 95	99.81 27	99.69 102	99.58 183	97.66 220	99.86 184	99.17 73	99.44 264	99.67 64

ACMM	98.09 11	99.46 60	99.38 67	99.72 74	99.80 56	99.69 74	99.13 162	99.65 123	98.99 158	99.64 118	99.72 92	99.39 24	99.86 184	98.23 151	99.81 148	99.60 116
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Regformer-4		99.45 62	99.44 58	99.50 155	99.52 183	98.94 221	99.17 145	99.53 194	99.64 59	99.76 74	99.60 175	98.96 79	99.90 123	98.91 106	99.84 121	99.67 64

COLMAP_ROB	98.06 12	99.45 62	99.37 70	99.70 82	99.83 37	99.70 70	99.38 84	99.78 57	99.53 79	99.67 108	99.78 65	99.19 49	99.86 184	97.32 225	99.87 107	99.55 142
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
tfpnnormal		99.43 64	99.38 67	99.60 124	99.87 28	99.75 48	99.59 56	99.78 57	99.71 41	99.90 23	99.69 112	98.85 91	99.90 123	97.25 235	99.78 164	99.15 261

HPM-MVS_fast		99.43 64	99.30 86	99.80 30	99.83 37	99.81 28	99.52 63	99.70 95	98.35 233	99.51 170	99.50 212	99.31 36	99.88 151	98.18 158	99.84 121	99.69 51

3Dnovator	99.15 2	99.43 64	99.36 73	99.65 98	99.39 233	99.42 141	99.70 23	99.56 175	99.23 126	99.35 205	99.80 52	99.17 51	99.95 43	98.21 153	99.84 121	99.59 125

Anonymous20240529		99.42 67	99.34 75	99.65 98	99.53 178	99.60 102	99.63 46	99.39 245	99.47 89	99.76 74	99.78 65	98.13 181	99.86 184	98.70 122	99.68 205	99.49 177

SixPastTwentyTwo		99.42 67	99.30 86	99.76 46	99.92 14	99.67 79	99.70 23	99.14 292	99.65 57	99.89 27	99.90 22	96.20 268	99.94 54	99.42 40	99.92 73	99.67 64

GBi-Net		99.42 67	99.31 81	99.73 68	99.49 199	99.77 40	99.68 32	99.70 95	99.44 95	99.62 129	99.83 41	97.21 239	99.90 123	98.96 99	99.90 83	99.53 154

test1		99.42 67	99.31 81	99.73 68	99.49 199	99.77 40	99.68 32	99.70 95	99.44 95	99.62 129	99.83 41	97.21 239	99.90 123	98.96 99	99.90 83	99.53 154

Regformer-3		99.41 71	99.41 63	99.40 186	99.52 183	98.70 239	99.17 145	99.44 228	99.62 63	99.75 79	99.60 175	98.90 86	99.85 202	98.89 107	99.84 121	99.65 82

MVSFormer		99.41 71	99.44 58	99.31 210	99.57 161	98.40 256	99.77 12	99.80 47	99.73 37	99.63 122	99.30 261	98.02 189	99.98 6	99.43 36	99.69 202	99.55 142

IterMVS-LS		99.41 71	99.47 52	99.25 222	99.81 50	98.09 275	98.85 212	99.76 65	99.62 63	99.83 47	99.64 140	98.54 134	99.97 17	99.15 77	99.99 13	99.68 57
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
SED-MVS		99.40 74	99.28 93	99.77 40	99.69 120	99.82 25	99.20 134	99.54 185	99.13 143	99.82 49	99.63 150	98.91 83	99.92 85	97.85 187	99.70 199	99.58 130

v148		99.40 74	99.41 63	99.39 189	99.76 84	98.94 221	99.09 173	99.59 159	99.17 135	99.81 56	99.61 168	98.41 152	99.69 299	99.32 51	99.94 61	99.53 154

NR-MVSNet		99.40 74	99.31 81	99.68 84	99.43 223	99.55 114	99.73 17	99.50 208	99.46 93	99.88 33	99.36 247	97.54 224	99.87 164	98.97 97	99.87 107	99.63 93

PVSNet_Blended_VisFu		99.40 74	99.38 67	99.44 172	99.90 20	98.66 242	98.94 203	99.91 8	97.97 258	99.79 64	99.73 86	99.05 69	99.97 17	99.15 77	99.99 13	99.68 57

EU-MVSNet		99.39 78	99.62 26	98.72 279	99.88 24	96.44 318	99.56 61	99.85 24	99.90 8	99.90 23	99.85 36	98.09 183	99.83 229	99.58 22	99.95 48	99.90 5

CHOSEN 1792x2688		99.39 78	99.30 86	99.65 98	99.88 24	99.25 180	98.78 227	99.88 16	98.66 197	99.96 9	99.79 58	97.45 227	99.93 67	99.34 47	99.99 13	99.78 31

EI-MVSNet		99.38 80	99.44 58	99.21 228	99.58 151	98.09 275	99.26 116	99.46 223	99.62 63	99.75 79	99.67 129	98.54 134	99.85 202	99.15 77	99.92 73	99.68 57

UGNet		99.38 80	99.34 75	99.49 158	98.90 314	98.90 229	99.70 23	99.35 256	99.86 17	98.57 297	99.81 50	98.50 144	99.93 67	99.38 42	99.98 22	99.66 74
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
UniMVSNet_NR-MVSNet		99.37 82	99.25 100	99.72 74	99.47 210	99.56 111	98.97 199	99.61 141	99.43 100	99.67 108	99.28 266	97.85 204	99.95 43	99.17 73	99.81 148	99.65 82

UniMVSNet (Re)		99.37 82	99.26 98	99.68 84	99.51 188	99.58 108	98.98 197	99.60 152	99.43 100	99.70 99	99.36 247	97.70 211	99.88 151	99.20 66	99.87 107	99.59 125

CSCG		99.37 82	99.29 91	99.60 124	99.71 110	99.46 126	99.43 77	99.85 24	98.79 186	99.41 194	99.60 175	98.92 81	99.92 85	98.02 168	99.92 73	99.43 203

PM-MVS		99.36 85	99.29 91	99.58 130	99.83 37	99.66 81	98.95 201	99.86 20	98.85 178	99.81 56	99.73 86	98.40 156	99.92 85	98.36 138	99.83 131	99.17 257

abl_6		99.36 85	99.23 103	99.75 55	99.71 110	99.74 54	99.33 94	99.76 65	99.07 151	99.65 116	99.63 150	99.09 61	99.92 85	97.13 243	99.76 170	99.58 130

new-patchmatchnet		99.35 87	99.57 38	98.71 281	99.82 43	96.62 316	98.55 247	99.75 71	99.50 82	99.88 33	99.87 31	99.31 36	99.88 151	99.43 36	100.00 1	99.62 105

Anonymous20231206		99.35 87	99.31 81	99.47 163	99.74 100	99.06 213	99.28 112	99.74 76	99.23 126	99.72 92	99.53 204	97.63 222	99.88 151	99.11 85	99.84 121	99.48 181

MTAPA		99.35 87	99.20 105	99.80 30	99.81 50	99.81 28	99.33 94	99.53 194	99.27 118	99.42 186	99.63 150	98.21 174	99.95 43	97.83 190	99.79 158	99.65 82

FMVSNet2		99.35 87	99.28 93	99.55 142	99.49 199	99.35 161	99.45 72	99.57 170	99.44 95	99.70 99	99.74 82	97.21 239	99.87 164	99.03 90	99.94 61	99.44 197

3Dnovator+	98.92 3	99.35 87	99.24 101	99.67 86	99.35 243	99.47 122	99.62 47	99.50 208	99.44 95	99.12 246	99.78 65	98.77 105	99.94 54	97.87 184	99.72 193	99.62 105

TSAR-MVS + MP.		99.34 92	99.24 101	99.63 109	99.82 43	99.37 154	99.26 116	99.35 256	98.77 189	99.57 145	99.70 106	99.27 44	99.88 151	97.71 197	99.75 173	99.65 82
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
Regformer-2		99.34 92	99.27 96	99.53 148	99.41 229	99.10 207	98.99 193	99.53 194	99.47 89	99.66 112	99.52 206	98.80 97	99.89 136	98.31 144	99.74 181	99.60 116

diffmvs		99.34 92	99.32 80	99.39 189	99.67 133	98.77 236	98.57 245	99.81 46	99.61 67	99.48 173	99.41 234	98.47 145	99.86 184	98.97 97	99.90 83	99.53 154

DELS-MVS		99.34 92	99.30 86	99.48 161	99.51 188	99.36 157	98.12 284	99.53 194	99.36 107	99.41 194	99.61 168	99.22 47	99.87 164	99.21 63	99.68 205	99.20 251
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
DU-MVS		99.33 96	99.21 104	99.71 78	99.43 223	99.56 111	98.83 215	99.53 194	99.38 104	99.67 108	99.36 247	97.67 216	99.95 43	99.17 73	99.81 148	99.63 93

ab-mvs		99.33 96	99.28 93	99.47 163	99.57 161	99.39 148	99.78 11	99.43 231	98.87 176	99.57 145	99.82 47	98.06 186	99.87 164	98.69 124	99.73 188	99.15 261

DVP-MVS		99.32 98	99.17 108	99.77 40	99.69 120	99.80 33	99.14 155	99.31 265	99.16 137	99.62 129	99.61 168	98.35 160	99.91 103	97.88 181	99.72 193	99.61 112

Regformer-1		99.32 98	99.27 96	99.47 163	99.41 229	98.95 220	98.99 193	99.48 215	99.48 84	99.66 112	99.52 206	98.78 102	99.87 164	98.36 138	99.74 181	99.60 116

APD-MVS_3200maxsize		99.31 100	99.16 109	99.74 60	99.53 178	99.75 48	99.27 115	99.61 141	99.19 131	99.57 145	99.64 140	98.76 106	99.90 123	97.29 227	99.62 224	99.56 139

zzz-MVS		99.30 101	99.14 113	99.80 30	99.81 50	99.81 28	98.73 232	99.53 194	99.27 118	99.42 186	99.63 150	98.21 174	99.95 43	97.83 190	99.79 158	99.65 82

SteuartSystems-ACMMP		99.30 101	99.14 113	99.76 46	99.87 28	99.66 81	99.18 140	99.60 152	98.55 208	99.57 145	99.67 129	99.03 71	99.94 54	97.01 247	99.80 153	99.69 51
Skip Steuart: Steuart Systems R&D Blog.
testgi		99.29 103	99.26 98	99.37 196	99.75 94	98.81 233	98.84 213	99.89 13	98.38 226	99.75 79	99.04 302	99.36 33	99.86 184	99.08 87	99.25 292	99.45 192

ACMMP_NAP		99.28 104	99.11 123	99.79 35	99.75 94	99.81 28	98.95 201	99.53 194	98.27 242	99.53 163	99.73 86	98.75 108	99.87 164	97.70 199	99.83 131	99.68 57

LCM-MVSNet-Re		99.28 104	99.15 112	99.67 86	99.33 258	99.76 46	99.34 92	99.97 2	98.93 168	99.91 21	99.79 58	98.68 114	99.93 67	96.80 260	99.56 239	99.30 232

mvs_anonymous		99.28 104	99.39 65	98.94 253	99.19 283	97.81 287	99.02 184	99.55 180	99.78 33	99.85 39	99.80 52	98.24 170	99.86 184	99.57 23	99.50 256	99.15 261

MVS_Test		99.28 104	99.31 81	99.19 231	99.35 243	98.79 235	99.36 90	99.49 213	99.17 135	99.21 232	99.67 129	98.78 102	99.66 319	99.09 86	99.66 216	99.10 271

SR-MVS-dyc-post		99.27 108	99.11 123	99.73 68	99.54 173	99.74 54	99.26 116	99.62 134	99.16 137	99.52 165	99.64 140	98.41 152	99.91 103	97.27 230	99.61 231	99.54 149

XVS		99.27 108	99.11 123	99.75 55	99.71 110	99.71 63	99.37 88	99.61 141	99.29 114	98.76 283	99.47 224	98.47 145	99.88 151	97.62 207	99.73 188	99.67 64

OPM-MVS		99.26 110	99.13 116	99.63 109	99.70 117	99.61 100	98.58 241	99.48 215	98.50 214	99.52 165	99.63 150	99.14 55	99.76 277	97.89 180	99.77 168	99.51 166

HFP-MVS		99.25 111	99.08 134	99.76 46	99.73 103	99.70 70	99.31 101	99.59 159	98.36 228	99.36 203	99.37 242	98.80 97	99.91 103	97.43 220	99.75 173	99.68 57

HPM-MVS		99.25 111	99.07 138	99.78 38	99.81 50	99.75 48	99.61 51	99.67 108	97.72 272	99.35 205	99.25 272	99.23 46	99.92 85	97.21 238	99.82 140	99.67 64
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ACMMP		99.25 111	99.08 134	99.74 60	99.79 66	99.68 77	99.50 65	99.65 123	98.07 252	99.52 165	99.69 112	98.57 129	99.92 85	97.18 240	99.79 158	99.63 93
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
LS3D		99.24 114	99.11 123	99.61 122	98.38 343	99.79 35	99.57 59	99.68 104	99.61 67	99.15 241	99.71 99	98.70 112	99.91 103	97.54 213	99.68 205	99.13 268

test1172		99.23 115	99.05 144	99.74 60	99.52 183	99.75 48	99.20 134	99.61 141	98.97 160	99.48 173	99.58 183	98.41 152	99.91 103	97.15 242	99.55 243	99.57 136

xiu_mvs_v1_base_debu		99.23 115	99.34 75	98.91 259	99.59 148	98.23 264	98.47 256	99.66 112	99.61 67	99.68 104	98.94 316	99.39 24	99.97 17	99.18 70	99.55 243	98.51 318

xiu_mvs_v1_base		99.23 115	99.34 75	98.91 259	99.59 148	98.23 264	98.47 256	99.66 112	99.61 67	99.68 104	98.94 316	99.39 24	99.97 17	99.18 70	99.55 243	98.51 318

xiu_mvs_v1_base_debi		99.23 115	99.34 75	98.91 259	99.59 148	98.23 264	98.47 256	99.66 112	99.61 67	99.68 104	98.94 316	99.39 24	99.97 17	99.18 70	99.55 243	98.51 318

region2R		99.23 115	99.05 144	99.77 40	99.76 84	99.70 70	99.31 101	99.59 159	98.41 222	99.32 212	99.36 247	98.73 111	99.93 67	97.29 227	99.74 181	99.67 64

ACMMPR		99.23 115	99.06 140	99.76 46	99.74 100	99.69 74	99.31 101	99.59 159	98.36 228	99.35 205	99.38 241	98.61 125	99.93 67	97.43 220	99.75 173	99.67 64

XVG-ACMP-BASELINE		99.23 115	99.10 131	99.63 109	99.82 43	99.58 108	98.83 215	99.72 88	98.36 228	99.60 137	99.71 99	98.92 81	99.91 103	97.08 245	99.84 121	99.40 209

CP-MVS		99.23 115	99.05 144	99.75 55	99.66 134	99.66 81	99.38 84	99.62 134	98.38 226	99.06 254	99.27 268	98.79 100	99.94 54	97.51 216	99.82 140	99.66 74

DeepC-MVS_fast	98.47 5	99.23 115	99.12 120	99.56 139	99.28 268	99.22 189	98.99 193	99.40 242	99.08 149	99.58 142	99.64 140	98.90 86	99.83 229	97.44 219	99.75 173	99.63 93
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ZNCC-MVS		99.22 124	99.04 150	99.77 40	99.76 84	99.73 57	99.28 112	99.56 175	98.19 247	99.14 243	99.29 264	98.84 92	99.92 85	97.53 215	99.80 153	99.64 88

D2MVS		99.22 124	99.19 106	99.29 213	99.69 120	98.74 237	98.81 220	99.41 235	98.55 208	99.68 104	99.69 112	98.13 181	99.87 164	98.82 112	99.98 22	99.24 241

LPG-MVS_test		99.22 124	99.05 144	99.74 60	99.82 43	99.63 92	99.16 151	99.73 79	97.56 278	99.64 118	99.69 112	99.37 30	99.89 136	96.66 268	99.87 107	99.69 51

CDS-MVSNet		99.22 124	99.13 116	99.50 155	99.35 243	99.11 203	98.96 200	99.54 185	99.46 93	99.61 135	99.70 106	96.31 265	99.83 229	99.34 47	99.88 99	99.55 142
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
test_0402		99.22 124	99.14 113	99.45 170	99.79 66	99.43 138	99.28 112	99.68 104	99.54 77	99.40 199	99.56 194	99.07 66	99.82 239	96.01 293	99.96 41	99.11 269

AllTest		99.21 129	99.07 138	99.63 109	99.78 72	99.64 88	99.12 166	99.83 32	98.63 200	99.63 122	99.72 92	98.68 114	99.75 281	96.38 282	99.83 131	99.51 166

XVG-OURS		99.21 129	99.06 140	99.65 98	99.82 43	99.62 94	97.87 312	99.74 76	98.36 228	99.66 112	99.68 123	99.71 9	99.90 123	96.84 258	99.88 99	99.43 203

Fast-Effi-MVS+-dtu		99.20 131	99.12 120	99.43 175	99.25 273	99.69 74	99.05 179	99.82 37	99.50 82	98.97 258	99.05 299	98.98 74	99.98 6	98.20 154	99.24 294	98.62 310

VDD-MVS		99.20 131	99.11 123	99.44 172	99.43 223	98.98 216	99.50 65	98.32 327	99.80 30	99.56 152	99.69 112	96.99 249	99.85 202	98.99 93	99.73 188	99.50 172

PGM-MVS		99.20 131	99.01 157	99.77 40	99.75 94	99.71 63	99.16 151	99.72 88	97.99 256	99.42 186	99.60 175	98.81 93	99.93 67	96.91 252	99.74 181	99.66 74

SR-MVS		99.19 134	99.00 160	99.74 60	99.51 188	99.72 61	99.18 140	99.60 152	98.85 178	99.47 175	99.58 183	98.38 157	99.92 85	96.92 251	99.54 249	99.57 136

SMA-MVS		99.19 134	99.00 160	99.73 68	99.46 215	99.73 57	99.13 162	99.52 202	97.40 288	99.57 145	99.64 140	98.93 80	99.83 229	97.61 209	99.79 158	99.63 93
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
pmmvs5		99.19 134	99.11 123	99.42 177	99.76 84	98.88 230	98.55 247	99.73 79	98.82 182	99.72 92	99.62 159	96.56 255	99.82 239	99.32 51	99.95 48	99.56 139

mPP-MVS		99.19 134	99.00 160	99.76 46	99.76 84	99.68 77	99.38 84	99.54 185	98.34 237	99.01 256	99.50 212	98.53 138	99.93 67	97.18 240	99.78 164	99.66 74

ETV-MVS		99.18 138	99.18 107	99.16 234	99.34 253	99.28 172	99.12 166	99.79 53	99.48 84	98.93 262	98.55 336	99.40 23	99.93 67	98.51 132	99.52 253	98.28 327

VNet		99.18 138	99.06 140	99.56 139	99.24 275	99.36 157	99.33 94	99.31 265	99.67 51	99.47 175	99.57 191	96.48 258	99.84 218	99.15 77	99.30 286	99.47 186

RPSCF		99.18 138	99.02 154	99.64 105	99.83 37	99.85 12	99.44 75	99.82 37	98.33 238	99.50 171	99.78 65	97.90 198	99.65 326	96.78 261	99.83 131	99.44 197

DeepPCF-MVS	98.42 6	99.18 138	99.02 154	99.67 86	99.22 277	99.75 48	97.25 339	99.47 219	98.72 194	99.66 112	99.70 106	99.29 39	99.63 329	98.07 167	99.81 148	99.62 105

EPP-MVSNet		99.17 142	99.00 160	99.66 93	99.80 56	99.43 138	99.70 23	99.24 282	99.48 84	99.56 152	99.77 72	94.89 281	99.93 67	98.72 121	99.89 91	99.63 93

GST-MVS		99.16 143	98.96 171	99.75 55	99.73 103	99.73 57	99.20 134	99.55 180	98.22 244	99.32 212	99.35 252	98.65 121	99.91 103	96.86 255	99.74 181	99.62 105

MVP-Stereo		99.16 143	99.08 134	99.43 175	99.48 205	99.07 211	99.08 176	99.55 180	98.63 200	99.31 214	99.68 123	98.19 177	99.78 267	98.18 158	99.58 237	99.45 192
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
XVG-OURS-SEG-HR		99.16 143	98.99 165	99.66 93	99.84 34	99.64 88	98.25 274	99.73 79	98.39 225	99.63 122	99.43 232	99.70 11	99.90 123	97.34 224	98.64 323	99.44 197

jason		99.16 143	99.11 123	99.32 207	99.75 94	98.44 253	98.26 273	99.39 245	98.70 195	99.74 87	99.30 261	98.54 134	99.97 17	98.48 133	99.82 140	99.55 142
jason: jason.
DPE-MVS		99.14 147	98.92 178	99.82 24	99.57 161	99.77 40	98.74 230	99.60 152	98.55 208	99.76 74	99.69 112	98.23 173	99.92 85	96.39 281	99.75 173	99.76 36

MP-MVS-pluss		99.14 147	98.92 178	99.80 30	99.83 37	99.83 21	98.61 237	99.63 131	96.84 307	99.44 180	99.58 183	98.81 93	99.91 103	97.70 199	99.82 140	99.67 64
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
pmmvs4		99.13 149	99.06 140	99.36 199	99.57 161	99.10 207	98.01 296	99.25 279	98.78 188	99.58 142	99.44 231	98.24 170	99.76 277	98.74 119	99.93 69	99.22 246

MVS_111021_LR		99.13 149	99.03 152	99.42 177	99.58 151	99.32 166	97.91 311	99.73 79	98.68 196	99.31 214	99.48 219	99.09 61	99.66 319	97.70 199	99.77 168	99.29 235

EIA-MVS		99.12 151	99.01 157	99.45 170	99.36 241	99.62 94	99.34 92	99.79 53	98.41 222	98.84 273	98.89 321	98.75 108	99.84 218	98.15 162	99.51 254	98.89 297

#test#		99.12 151	98.90 182	99.76 46	99.73 103	99.70 70	99.10 169	99.59 159	97.60 277	99.36 203	99.37 242	98.80 97	99.91 103	96.84 258	99.75 173	99.68 57

TSAR-MVS + GP.		99.12 151	99.04 150	99.38 193	99.34 253	99.16 198	98.15 280	99.29 270	98.18 248	99.63 122	99.62 159	99.18 50	99.68 310	98.20 154	99.74 181	99.30 232

MVS_111021_HR		99.12 151	99.02 154	99.40 186	99.50 194	99.11 203	97.92 309	99.71 91	98.76 192	99.08 250	99.47 224	99.17 51	99.54 339	97.85 187	99.76 170	99.54 149

xxxxxxxxxxxxxcwj		99.11 155	98.96 171	99.54 146	99.53 178	99.25 180	98.29 270	99.76 65	99.07 151	99.42 186	99.61 168	98.86 89	99.87 164	96.45 279	99.68 205	99.49 177

CANet		99.11 155	99.05 144	99.28 215	98.83 323	98.56 246	98.71 235	99.41 235	99.25 122	99.23 226	99.22 279	97.66 220	99.94 54	99.19 68	99.97 30	99.33 226

WR-MVS		99.11 155	98.93 174	99.66 93	99.30 264	99.42 141	98.42 262	99.37 252	99.04 156	99.57 145	99.20 283	96.89 251	99.86 184	98.66 126	99.87 107	99.70 48

PHI-MVS		99.11 155	98.95 173	99.59 126	99.13 291	99.59 105	99.17 145	99.65 123	97.88 264	99.25 222	99.46 227	98.97 76	99.80 260	97.26 232	99.82 140	99.37 217

SF-MVS		99.10 159	98.93 174	99.62 118	99.58 151	99.51 117	99.13 162	99.65 123	97.97 258	99.42 186	99.61 168	98.86 89	99.87 164	96.45 279	99.68 205	99.49 177

CS-MVS		99.09 160	99.03 152	99.25 222	99.45 218	99.49 119	99.41 78	99.82 37	99.10 148	98.03 324	98.48 340	99.30 38	99.89 136	98.30 145	99.41 270	98.35 324

MSDG		99.08 161	98.98 168	99.37 196	99.60 146	99.13 201	97.54 325	99.74 76	98.84 181	99.53 163	99.55 200	99.10 59	99.79 263	97.07 246	99.86 114	99.18 255

Effi-MVS+-dtu		99.07 162	98.92 178	99.52 149	98.89 317	99.78 38	99.15 153	99.66 112	99.34 108	98.92 265	99.24 277	97.69 213	99.98 6	98.11 164	99.28 288	98.81 304

Effi-MVS+		99.06 163	98.97 169	99.34 201	99.31 260	98.98 216	98.31 269	99.91 8	98.81 183	98.79 279	98.94 316	99.14 55	99.84 218	98.79 114	98.74 319	99.20 251

MP-MVS		99.06 163	98.83 191	99.76 46	99.76 84	99.71 63	99.32 97	99.50 208	98.35 233	98.97 258	99.48 219	98.37 158	99.92 85	95.95 299	99.75 173	99.63 93
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MDA-MVSNet-bldmvs		99.06 163	99.05 144	99.07 244	99.80 56	97.83 286	98.89 205	99.72 88	99.29 114	99.63 122	99.70 106	96.47 259	99.89 136	98.17 160	99.82 140	99.50 172

MSLP-MVS++		99.05 166	99.09 132	98.91 259	99.21 278	98.36 260	98.82 219	99.47 219	98.85 178	98.90 268	99.56 194	98.78 102	99.09 350	98.57 129	99.68 205	99.26 238

1112_ss		99.05 166	98.84 189	99.67 86	99.66 134	99.29 170	98.52 252	99.82 37	97.65 275	99.43 184	99.16 286	96.42 261	99.91 103	99.07 88	99.84 121	99.80 24

ACMP	97.51 14	99.05 166	98.84 189	99.67 86	99.78 72	99.55 114	98.88 206	99.66 112	97.11 301	99.47 175	99.60 175	99.07 66	99.89 136	96.18 288	99.85 117	99.58 130
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MSP-MVS		99.04 169	98.79 196	99.81 27	99.78 72	99.73 57	99.35 91	99.57 170	98.54 211	99.54 159	98.99 306	96.81 252	99.93 67	96.97 249	99.53 251	99.77 32

PVSNet_BlendedMVS		99.03 170	99.01 157	99.09 240	99.54 173	97.99 279	98.58 241	99.82 37	97.62 276	99.34 208	99.71 99	98.52 141	99.77 275	97.98 173	99.97 30	99.52 164

IS-MVSNet		99.03 170	98.85 187	99.55 142	99.80 56	99.25 180	99.73 17	99.15 291	99.37 105	99.61 135	99.71 99	94.73 284	99.81 255	97.70 199	99.88 99	99.58 130

xiu_mvs_v2_base		99.02 172	99.11 123	98.77 276	99.37 239	98.09 275	98.13 283	99.51 205	99.47 89	99.42 186	98.54 337	99.38 28	99.97 17	98.83 110	99.33 283	98.24 329

Fast-Effi-MVS+		99.02 172	98.87 185	99.46 166	99.38 236	99.50 118	99.04 181	99.79 53	97.17 297	98.62 292	98.74 329	99.34 34	99.95 43	98.32 143	99.41 270	98.92 295

canonicalmvs		99.02 172	99.00 160	99.09 240	99.10 299	98.70 239	99.61 51	99.66 112	99.63 62	98.64 291	97.65 351	99.04 70	99.54 339	98.79 114	98.92 308	99.04 285

MCST-MVS		99.02 172	98.81 193	99.65 98	99.58 151	99.49 119	98.58 241	99.07 295	98.40 224	99.04 255	99.25 272	98.51 143	99.80 260	97.31 226	99.51 254	99.65 82

SD-MVS		99.01 176	99.30 86	98.15 301	99.50 194	99.40 146	98.94 203	99.61 141	99.22 129	99.75 79	99.82 47	99.54 20	95.51 356	97.48 217	99.87 107	99.54 149
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
LF4IMVS		99.01 176	98.92 178	99.27 217	99.71 110	99.28 172	98.59 240	99.77 60	98.32 239	99.39 200	99.41 234	98.62 123	99.84 218	96.62 271	99.84 121	98.69 308

IterMVS-SCA-FT		99.00 178	99.16 109	98.51 286	99.75 94	95.90 326	98.07 291	99.84 30	99.84 22	99.89 27	99.73 86	96.01 272	99.99 4	99.33 49	100.00 1	99.63 93

MS-PatchMatch		99.00 178	98.97 169	99.09 240	99.11 298	98.19 267	98.76 229	99.33 259	98.49 216	99.44 180	99.58 183	98.21 174	99.69 299	98.20 154	99.62 224	99.39 212

PS-MVSNAJ		99.00 178	99.08 134	98.76 277	99.37 239	98.10 274	98.00 298	99.51 205	99.47 89	99.41 194	98.50 339	99.28 41	99.97 17	98.83 110	99.34 281	98.20 333

CNVR-MVS		98.99 181	98.80 195	99.56 139	99.25 273	99.43 138	98.54 250	99.27 274	98.58 205	98.80 278	99.43 232	98.53 138	99.70 293	97.22 237	99.59 236	99.54 149

VDDNet		98.97 182	98.82 192	99.42 177	99.71 110	98.81 233	99.62 47	98.68 311	99.81 27	99.38 201	99.80 52	94.25 288	99.85 202	98.79 114	99.32 284	99.59 125

IterMVS		98.97 182	99.16 109	98.42 290	99.74 100	95.64 329	98.06 293	99.83 32	99.83 25	99.85 39	99.74 82	96.10 271	99.99 4	99.27 61	100.00 1	99.63 93
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TinyColmap		98.97 182	98.93 174	99.07 244	99.46 215	98.19 267	97.75 316	99.75 71	98.79 186	99.54 159	99.70 106	98.97 76	99.62 330	96.63 270	99.83 131	99.41 207

HPM-MVS++		98.96 185	98.70 203	99.74 60	99.52 183	99.71 63	98.86 210	99.19 287	98.47 218	98.59 295	99.06 298	98.08 185	99.91 103	96.94 250	99.60 234	99.60 116

lupinMVS		98.96 185	98.87 185	99.24 225	99.57 161	98.40 256	98.12 284	99.18 288	98.28 241	99.63 122	99.13 288	98.02 189	99.97 17	98.22 152	99.69 202	99.35 223

USDC		98.96 185	98.93 174	99.05 246	99.54 173	97.99 279	97.07 342	99.80 47	98.21 245	99.75 79	99.77 72	98.43 150	99.64 328	97.90 179	99.88 99	99.51 166

YYNet1		98.95 188	98.99 165	98.84 269	99.64 138	97.14 306	98.22 276	99.32 261	98.92 170	99.59 140	99.66 133	97.40 229	99.83 229	98.27 148	99.90 83	99.55 142

MDA-MVSNet_test_wron		98.95 188	98.99 165	98.85 267	99.64 138	97.16 305	98.23 275	99.33 259	98.93 168	99.56 152	99.66 133	97.39 231	99.83 229	98.29 146	99.88 99	99.55 142

Test_1112_low_res		98.95 188	98.73 198	99.63 109	99.68 128	99.15 200	98.09 288	99.80 47	97.14 299	99.46 178	99.40 236	96.11 270	99.89 136	99.01 92	99.84 121	99.84 15

CANet_DTU		98.91 191	98.85 187	99.09 240	98.79 329	98.13 270	98.18 277	99.31 265	99.48 84	98.86 271	99.51 209	96.56 255	99.95 43	99.05 89	99.95 48	99.19 253

HyFIR lowres test		98.91 191	98.64 206	99.73 68	99.85 33	99.47 122	98.07 291	99.83 32	98.64 199	99.89 27	99.60 175	92.57 302	100.00 1	99.33 49	99.97 30	99.72 42

HQP_MVS		98.90 193	98.68 205	99.55 142	99.58 151	99.24 185	98.80 223	99.54 185	98.94 165	99.14 243	99.25 272	97.24 237	99.82 239	95.84 302	99.78 164	99.60 116

sss		98.90 193	98.77 197	99.27 217	99.48 205	98.44 253	98.72 233	99.32 261	97.94 262	99.37 202	99.35 252	96.31 265	99.91 103	98.85 109	99.63 223	99.47 186

OMC-MVS		98.90 193	98.72 199	99.44 172	99.39 233	99.42 141	98.58 241	99.64 129	97.31 293	99.44 180	99.62 159	98.59 127	99.69 299	96.17 289	99.79 158	99.22 246

ppachtmachnet_test		98.89 196	99.12 120	98.20 300	99.66 134	95.24 333	97.63 321	99.68 104	99.08 149	99.78 67	99.62 159	98.65 121	99.88 151	98.02 168	99.96 41	99.48 181

MVS_0304		98.88 197	98.71 200	99.39 189	98.85 321	98.91 228	99.45 72	99.30 268	98.56 206	97.26 342	99.68 123	96.18 269	99.96 34	99.17 73	99.94 61	99.29 235

new_pmnet		98.88 197	98.89 183	98.84 269	99.70 117	97.62 293	98.15 280	99.50 208	97.98 257	99.62 129	99.54 202	98.15 180	99.94 54	97.55 212	99.84 121	98.95 292

K. test v3		98.87 199	98.60 209	99.69 83	99.93 13	99.46 126	99.74 16	94.97 350	99.78 33	99.88 33	99.88 29	93.66 294	99.97 17	99.61 19	99.95 48	99.64 88

APD-MVS		98.87 199	98.59 211	99.71 78	99.50 194	99.62 94	99.01 186	99.57 170	96.80 309	99.54 159	99.63 150	98.29 166	99.91 103	95.24 316	99.71 197	99.61 112
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
our_test_3		98.85 201	99.09 132	98.13 302	99.66 134	94.90 336	97.72 317	99.58 168	99.07 151	99.64 118	99.62 159	98.19 177	99.93 67	98.41 135	99.95 48	99.55 142

mvs-test1		98.83 202	98.70 203	99.22 227	98.89 317	99.65 86	98.88 206	99.66 112	99.34 108	98.29 308	98.94 316	97.69 213	99.96 34	98.11 164	98.54 327	98.04 337

UnsupCasMVSNet_eth		98.83 202	98.57 215	99.59 126	99.68 128	99.45 131	98.99 193	99.67 108	99.48 84	99.55 157	99.36 247	94.92 280	99.86 184	98.95 103	96.57 348	99.45 192

NCCC		98.82 204	98.57 215	99.58 130	99.21 278	99.31 167	98.61 237	99.25 279	98.65 198	98.43 305	99.26 270	97.86 202	99.81 255	96.55 272	99.27 291	99.61 112

PMVS	92.94 21	98.82 204	98.81 193	98.85 267	99.84 34	97.99 279	99.20 134	99.47 219	99.71 41	99.42 186	99.82 47	98.09 183	99.47 344	93.88 335	99.85 117	99.07 282
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
FMVSNet3		98.80 206	98.63 208	99.32 207	99.13 291	98.72 238	99.10 169	99.48 215	99.23 126	99.62 129	99.64 140	92.57 302	99.86 184	98.96 99	99.90 83	99.39 212

Patchmtry		98.78 207	98.54 218	99.49 158	98.89 317	99.19 196	99.32 97	99.67 108	99.65 57	99.72 92	99.79 58	91.87 309	99.95 43	98.00 172	99.97 30	99.33 226

ETH3D-3000-0.1		98.77 208	98.50 222	99.59 126	99.47 210	99.53 116	98.77 228	99.60 152	97.33 292	99.23 226	99.50 212	97.91 197	99.83 229	95.02 320	99.67 212	99.41 207

Vis-MVSNet (Re-imp)		98.77 208	98.58 214	99.34 201	99.78 72	98.88 230	99.61 51	99.56 175	99.11 147	99.24 225	99.56 194	93.00 300	99.78 267	97.43 220	99.89 91	99.35 223

CLD-MVS		98.76 210	98.57 215	99.33 203	99.57 161	98.97 218	97.53 327	99.55 180	96.41 313	99.27 220	99.13 288	99.07 66	99.78 267	96.73 264	99.89 91	99.23 244
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Anonymous202405211		98.75 211	98.46 224	99.63 109	99.34 253	99.66 81	99.47 71	97.65 336	99.28 117	99.56 152	99.50 212	93.15 297	99.84 218	98.62 127	99.58 237	99.40 209

RRT_MVS		98.75 211	98.54 218	99.41 184	98.14 352	98.61 245	98.98 197	99.66 112	99.31 113	99.84 42	99.75 79	91.98 306	99.98 6	99.20 66	99.95 48	99.62 105

CPTT-MVS		98.74 213	98.44 226	99.64 105	99.61 144	99.38 151	99.18 140	99.55 180	96.49 312	99.27 220	99.37 242	97.11 245	99.92 85	95.74 306	99.67 212	99.62 105

F-COLMAP		98.74 213	98.45 225	99.62 118	99.57 161	99.47 122	98.84 213	99.65 123	96.31 316	98.93 262	99.19 285	97.68 215	99.87 164	96.52 274	99.37 278	99.53 154

N_pmnet		98.73 215	98.53 220	99.35 200	99.72 107	98.67 241	98.34 265	94.65 351	98.35 233	99.79 64	99.68 123	98.03 187	99.93 67	98.28 147	99.92 73	99.44 197

cl_fuxian		98.72 216	98.71 200	98.72 279	99.12 293	97.22 304	97.68 320	99.56 175	98.90 172	99.54 159	99.48 219	96.37 264	99.73 285	97.88 181	99.88 99	99.21 248

PVSNet_Blended		98.70 217	98.59 211	99.02 248	99.54 173	97.99 279	97.58 324	99.82 37	95.70 325	99.34 208	98.98 309	98.52 141	99.77 275	97.98 173	99.83 131	99.30 232

eth_miper_zixun_eth		98.68 218	98.71 200	98.60 283	99.10 299	96.84 313	97.52 329	99.54 185	98.94 165	99.58 142	99.48 219	96.25 267	99.76 277	98.01 171	99.93 69	99.21 248

PatchMatch-RL		98.68 218	98.47 223	99.30 212	99.44 221	99.28 172	98.14 282	99.54 185	97.12 300	99.11 247	99.25 272	97.80 207	99.70 293	96.51 275	99.30 286	98.93 294

miper_lstm_enhance		98.65 220	98.60 209	98.82 274	99.20 281	97.33 301	97.78 315	99.66 112	99.01 157	99.59 140	99.50 212	94.62 285	99.85 202	98.12 163	99.90 83	99.26 238

test_prior3		98.62 221	98.34 237	99.46 166	99.35 243	99.22 189	97.95 305	99.39 245	97.87 265	98.05 321	99.05 299	97.90 198	99.69 299	95.99 295	99.49 258	99.48 181

CVMVSNet		98.61 222	98.88 184	97.80 310	99.58 151	93.60 341	99.26 116	99.64 129	99.66 55	99.72 92	99.67 129	93.26 296	99.93 67	99.30 55	99.81 148	99.87 10

Patchmatch-RL test		98.60 223	98.36 234	99.33 203	99.77 80	99.07 211	98.27 272	99.87 18	98.91 171	99.74 87	99.72 92	90.57 326	99.79 263	98.55 130	99.85 117	99.11 269

RPMNet		98.60 223	98.53 220	98.83 271	99.05 304	98.12 271	99.30 104	99.62 134	99.86 17	99.16 239	99.74 82	92.53 304	99.92 85	98.75 118	98.77 315	98.44 321

AdaColmap		98.60 223	98.35 236	99.38 193	99.12 293	99.22 189	98.67 236	99.42 234	97.84 269	98.81 276	99.27 268	97.32 235	99.81 255	95.14 317	99.53 251	99.10 271

miper_ehance_all_eth		98.59 226	98.59 211	98.59 284	98.98 310	97.07 307	97.49 330	99.52 202	98.50 214	99.52 165	99.37 242	96.41 263	99.71 291	97.86 185	99.62 224	99.00 290

WTY-MVS		98.59 226	98.37 233	99.26 219	99.43 223	98.40 256	98.74 230	99.13 294	98.10 250	99.21 232	99.24 277	94.82 282	99.90 123	97.86 185	98.77 315	99.49 177

CNLPA		98.57 228	98.34 237	99.28 215	99.18 285	99.10 207	98.34 265	99.41 235	98.48 217	98.52 300	98.98 309	97.05 247	99.78 267	95.59 308	99.50 256	98.96 291

testtj		98.56 229	98.17 251	99.72 74	99.45 218	99.60 102	98.88 206	99.50 208	96.88 304	99.18 238	99.48 219	97.08 246	99.92 85	93.69 336	99.38 274	99.63 93

1121		98.56 229	98.24 242	99.52 149	99.49 199	99.24 185	99.30 104	99.22 284	95.77 323	98.52 300	99.29 264	97.39 231	99.85 202	95.79 304	99.34 281	99.46 190

CDPH-MVS		98.56 229	98.20 246	99.61 122	99.50 194	99.46 126	98.32 268	99.41 235	95.22 330	99.21 232	99.10 295	98.34 162	99.82 239	95.09 319	99.66 216	99.56 139

UnsupCasMVSNet_bld		98.55 232	98.27 241	99.40 186	99.56 171	99.37 154	97.97 304	99.68 104	97.49 284	99.08 250	99.35 252	95.41 279	99.82 239	97.70 199	98.19 335	99.01 289

cl-mvsnet_		98.54 233	98.41 229	98.92 257	99.03 307	97.80 288	97.46 331	99.59 159	98.90 172	99.60 137	99.46 227	93.85 291	99.78 267	97.97 175	99.89 91	99.17 257

cl-mvsnet1		98.54 233	98.42 228	98.92 257	99.03 307	97.80 288	97.46 331	99.59 159	98.90 172	99.60 137	99.46 227	93.87 290	99.78 267	97.97 175	99.89 91	99.18 255

MG-MVS		98.52 235	98.39 231	98.94 253	99.15 288	97.39 300	98.18 277	99.21 286	98.89 175	99.23 226	99.63 150	97.37 233	99.74 283	94.22 329	99.61 231	99.69 51

ETH3D cwj APD-0.16		98.50 236	98.16 252	99.51 152	99.04 306	99.39 148	98.47 256	99.47 219	96.70 311	98.78 281	99.33 256	97.62 223	99.86 184	94.69 325	99.38 274	99.28 237

DP-MVS Recon		98.50 236	98.23 243	99.31 210	99.49 199	99.46 126	98.56 246	99.63 131	94.86 336	98.85 272	99.37 242	97.81 206	99.59 336	96.08 290	99.44 264	98.88 298

CMPMVS	77.52 23	98.50 236	98.19 249	99.41 184	98.33 345	99.56 111	99.01 186	99.59 159	95.44 327	99.57 145	99.80 52	95.64 276	99.46 346	96.47 278	99.92 73	99.21 248
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
114514_t		98.49 239	98.11 254	99.64 105	99.73 103	99.58 108	99.24 124	99.76 65	89.94 349	99.42 186	99.56 194	97.76 210	99.86 184	97.74 195	99.82 140	99.47 186

PMMVS		98.49 239	98.29 240	99.11 238	98.96 311	98.42 255	97.54 325	99.32 261	97.53 281	98.47 304	98.15 346	97.88 201	99.82 239	97.46 218	99.24 294	99.09 274

MVSTER		98.47 241	98.22 244	99.24 225	99.06 303	98.35 261	99.08 176	99.46 223	99.27 118	99.75 79	99.66 133	88.61 336	99.85 202	99.14 83	99.92 73	99.52 164

LFMVS		98.46 242	98.19 249	99.26 219	99.24 275	98.52 249	99.62 47	96.94 343	99.87 15	99.31 214	99.58 183	91.04 317	99.81 255	98.68 125	99.42 269	99.45 192

PatchT		98.45 243	98.32 239	98.83 271	98.94 312	98.29 262	99.24 124	98.82 306	99.84 22	99.08 250	99.76 75	91.37 312	99.94 54	98.82 112	99.00 305	98.26 328

MIMVSNet		98.43 244	98.20 246	99.11 238	99.53 178	98.38 259	99.58 58	98.61 315	98.96 163	99.33 210	99.76 75	90.92 319	99.81 255	97.38 223	99.76 170	99.15 261

PVSNet	97.47 15	98.42 245	98.44 226	98.35 293	99.46 215	96.26 320	96.70 347	99.34 258	97.68 274	99.00 257	99.13 288	97.40 229	99.72 287	97.59 211	99.68 205	99.08 277

CHOSEN 280x420		98.41 246	98.41 229	98.40 291	99.34 253	95.89 327	96.94 344	99.44 228	98.80 185	99.25 222	99.52 206	93.51 295	99.98 6	98.94 104	99.98 22	99.32 229

BH-RMVSNet		98.41 246	98.14 253	99.21 228	99.21 278	98.47 250	98.60 239	98.26 328	98.35 233	98.93 262	99.31 259	97.20 242	99.66 319	94.32 327	99.10 299	99.51 166

QAPM		98.40 248	97.99 259	99.65 98	99.39 233	99.47 122	99.67 36	99.52 202	91.70 346	98.78 281	99.80 52	98.55 132	99.95 43	94.71 324	99.75 173	99.53 154

API-MVS		98.38 249	98.39 231	98.35 293	98.83 323	99.26 176	99.14 155	99.18 288	98.59 204	98.66 290	98.78 327	98.61 125	99.57 338	94.14 330	99.56 239	96.21 349

HQP-MVS		98.36 250	98.02 258	99.39 189	99.31 260	98.94 221	97.98 301	99.37 252	97.45 285	98.15 315	98.83 324	96.67 253	99.70 293	94.73 322	99.67 212	99.53 154

PAPM_NR		98.36 250	98.04 257	99.33 203	99.48 205	98.93 225	98.79 226	99.28 273	97.54 280	98.56 298	98.57 334	97.12 244	99.69 299	94.09 331	98.90 310	99.38 214

PLC	97.35 16	98.36 250	97.99 259	99.48 161	99.32 259	99.24 185	98.50 254	99.51 205	95.19 332	98.58 296	98.96 314	96.95 250	99.83 229	95.63 307	99.25 292	99.37 217
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
train_agg		98.35 253	97.95 263	99.57 135	99.35 243	99.35 161	98.11 286	99.41 235	94.90 334	97.92 327	98.99 306	98.02 189	99.85 202	95.38 314	99.44 264	99.50 172

CR-MVSNet		98.35 253	98.20 246	98.83 271	99.05 304	98.12 271	99.30 104	99.67 108	97.39 289	99.16 239	99.79 58	91.87 309	99.91 103	98.78 117	98.77 315	98.44 321

agg_prior1		98.33 255	97.92 269	99.57 135	99.35 243	99.36 157	97.99 300	99.39 245	94.85 337	97.76 336	98.98 309	98.03 187	99.85 202	95.49 310	99.44 264	99.51 166

DPM-MVS		98.28 256	97.94 267	99.32 207	99.36 241	99.11 203	97.31 337	98.78 308	96.88 304	98.84 273	99.11 294	97.77 209	99.61 334	94.03 333	99.36 279	99.23 244

alignmvs		98.28 256	97.96 262	99.25 222	99.12 293	98.93 225	99.03 183	98.42 323	99.64 59	98.72 286	97.85 349	90.86 322	99.62 330	98.88 108	99.13 297	99.19 253

test_yl		98.25 258	97.95 263	99.13 236	99.17 286	98.47 250	99.00 188	98.67 313	98.97 160	99.22 230	99.02 304	91.31 313	99.69 299	97.26 232	98.93 306	99.24 241

DCV-MVSNet		98.25 258	97.95 263	99.13 236	99.17 286	98.47 250	99.00 188	98.67 313	98.97 160	99.22 230	99.02 304	91.31 313	99.69 299	97.26 232	98.93 306	99.24 241

MAR-MVS		98.24 260	97.92 269	99.19 231	98.78 331	99.65 86	99.17 145	99.14 292	95.36 328	98.04 323	98.81 326	97.47 226	99.72 287	95.47 312	99.06 300	98.21 331
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
OpenMVS	98.12 10	98.23 261	97.89 273	99.26 219	99.19 283	99.26 176	99.65 44	99.69 101	91.33 347	98.14 319	99.77 72	98.28 167	99.96 34	95.41 313	99.55 243	98.58 314

BH-untuned		98.22 262	98.09 255	98.58 285	99.38 236	97.24 303	98.55 247	98.98 301	97.81 270	99.20 237	98.76 328	97.01 248	99.65 326	94.83 321	98.33 331	98.86 300

HY-MVS	98.23 9	98.21 263	97.95 263	98.99 249	99.03 307	98.24 263	99.61 51	98.72 310	96.81 308	98.73 285	99.51 209	94.06 289	99.86 184	96.91 252	98.20 333	98.86 300

EPNet		98.13 264	97.77 277	99.18 233	94.57 358	97.99 279	99.24 124	97.96 331	99.74 36	97.29 341	99.62 159	93.13 298	99.97 17	98.59 128	99.83 131	99.58 130
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SCA		98.11 265	98.36 234	97.36 321	99.20 281	92.99 344	98.17 279	98.49 321	98.24 243	99.10 249	99.57 191	96.01 272	99.94 54	96.86 255	99.62 224	99.14 265

Patchmatch-test		98.10 266	97.98 261	98.48 288	99.27 270	96.48 317	99.40 80	99.07 295	98.81 183	99.23 226	99.57 191	90.11 330	99.87 164	96.69 265	99.64 221	99.09 274

pmmvs3		98.08 267	97.80 274	98.91 259	99.41 229	97.69 292	97.87 312	99.66 112	95.87 321	99.50 171	99.51 209	90.35 328	99.97 17	98.55 130	99.47 261	99.08 277

JIA-IIPM		98.06 268	97.92 269	98.50 287	98.59 338	97.02 308	98.80 223	98.51 319	99.88 14	97.89 329	99.87 31	91.89 308	99.90 123	98.16 161	97.68 344	98.59 312

miper_enhance_ethall		98.03 269	97.94 267	98.32 295	98.27 346	96.43 319	96.95 343	99.41 235	96.37 315	99.43 184	98.96 314	94.74 283	99.69 299	97.71 197	99.62 224	98.83 303

TAPA-MVS	97.92 13	98.03 269	97.55 283	99.46 166	99.47 210	99.44 133	98.50 254	99.62 134	86.79 350	99.07 253	99.26 270	98.26 169	99.62 330	97.28 229	99.73 188	99.31 231
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
1314		98.00 271	97.90 272	98.27 299	98.90 314	97.45 298	99.30 104	99.06 297	94.98 333	97.21 343	99.12 292	98.43 150	99.67 315	95.58 309	98.56 326	97.71 341

GA-MVS		97.99 272	97.68 280	98.93 256	99.52 183	98.04 278	97.19 341	99.05 298	98.32 239	98.81 276	98.97 312	89.89 333	99.41 347	98.33 142	99.05 301	99.34 225

MVS-HIRNet		97.86 273	98.22 244	96.76 327	99.28 268	91.53 353	98.38 264	92.60 356	99.13 143	99.31 214	99.96 11	97.18 243	99.68 310	98.34 141	99.83 131	99.07 282

AUN-MVS		97.82 274	97.38 285	99.14 235	99.27 270	98.53 247	98.72 233	99.02 299	98.10 250	97.18 344	99.03 303	89.26 335	99.85 202	97.94 177	97.91 341	99.03 286

FMVSNet5		97.80 275	97.25 289	99.42 177	98.83 323	98.97 218	99.38 84	99.80 47	98.87 176	99.25 222	99.69 112	80.60 357	99.91 103	98.96 99	99.90 83	99.38 214

ADS-MVSNet2		97.78 276	97.66 282	98.12 303	99.14 289	95.36 331	99.22 131	98.75 309	96.97 302	98.25 311	99.64 140	90.90 320	99.94 54	96.51 275	99.56 239	99.08 277

ETH3 D test6400		97.76 277	97.19 292	99.50 155	99.38 236	99.26 176	98.34 265	99.49 213	92.99 343	98.54 299	99.20 283	95.92 274	99.82 239	91.14 343	99.66 216	99.40 209

baseline1		97.73 278	97.33 286	98.96 251	99.30 264	97.73 290	99.40 80	98.42 323	99.33 111	99.46 178	99.21 281	91.18 315	99.82 239	98.35 140	91.26 353	99.32 229

tpmrst		97.73 278	98.07 256	96.73 329	98.71 335	92.00 348	99.10 169	98.86 303	98.52 212	98.92 265	99.54 202	91.90 307	99.82 239	98.02 168	99.03 303	98.37 323

ADS-MVSNet		97.72 280	97.67 281	97.86 308	99.14 289	94.65 337	99.22 131	98.86 303	96.97 302	98.25 311	99.64 140	90.90 320	99.84 218	96.51 275	99.56 239	99.08 277

PatchmatchNet		97.65 281	97.80 274	97.18 324	98.82 326	92.49 346	99.17 145	98.39 325	98.12 249	98.79 279	99.58 183	90.71 324	99.89 136	97.23 236	99.41 270	99.16 259
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tttt0517		97.62 282	97.20 291	98.90 265	99.76 84	97.40 299	99.48 69	94.36 352	99.06 155	99.70 99	99.49 217	84.55 351	99.94 54	98.73 120	99.65 219	99.36 220

EPNet_dtu		97.62 282	97.79 276	97.11 326	96.67 357	92.31 347	98.51 253	98.04 329	99.24 124	95.77 351	99.47 224	93.78 293	99.66 319	98.98 95	99.62 224	99.37 217
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
wuyk23d		97.58 284	99.13 116	92.93 338	99.69 120	99.49 119	99.52 63	99.77 60	97.97 258	99.96 9	99.79 58	99.84 3	99.94 54	95.85 301	99.82 140	79.36 352

cl-mvsnet2		97.56 285	97.28 287	98.40 291	98.37 344	96.75 314	97.24 340	99.37 252	97.31 293	99.41 194	99.22 279	87.30 338	99.37 348	97.70 199	99.62 224	99.08 277

PAPR		97.56 285	97.07 294	99.04 247	98.80 328	98.11 273	97.63 321	99.25 279	94.56 340	98.02 325	98.25 345	97.43 228	99.68 310	90.90 344	98.74 319	99.33 226

thisisatest0530		97.45 287	96.95 298	98.94 253	99.68 128	97.73 290	99.09 173	94.19 354	98.61 203	99.56 152	99.30 261	84.30 352	99.93 67	98.27 148	99.54 249	99.16 259

TR-MVS		97.44 288	97.15 293	98.32 295	98.53 340	97.46 297	98.47 256	97.91 333	96.85 306	98.21 314	98.51 338	96.42 261	99.51 342	92.16 339	97.29 346	97.98 338

tpmvs		97.39 289	97.69 279	96.52 332	98.41 342	91.76 350	99.30 104	98.94 302	97.74 271	97.85 332	99.55 200	92.40 305	99.73 285	96.25 287	98.73 321	98.06 336

test0.0.03 1		97.37 290	96.91 301	98.74 278	97.72 353	97.57 294	97.60 323	97.36 342	98.00 254	99.21 232	98.02 347	90.04 331	99.79 263	98.37 137	95.89 351	98.86 300

OpenMVS_ROB	97.31 17	97.36 291	96.84 302	98.89 266	99.29 266	99.45 131	98.87 209	99.48 215	86.54 352	99.44 180	99.74 82	97.34 234	99.86 184	91.61 340	99.28 288	97.37 345

RRT_test8_iter05		97.35 292	97.25 289	97.63 315	98.81 327	93.13 343	99.26 116	99.89 13	99.51 81	99.83 47	99.68 123	79.03 360	99.88 151	99.53 27	99.72 193	99.89 9

BH-w/o		97.20 293	97.01 296	97.76 311	99.08 302	95.69 328	98.03 295	98.52 318	95.76 324	97.96 326	98.02 347	95.62 277	99.47 344	92.82 338	97.25 347	98.12 335

test-LLR		97.15 294	96.95 298	97.74 313	98.18 349	95.02 334	97.38 333	96.10 344	98.00 254	97.81 333	98.58 332	90.04 331	99.91 103	97.69 205	98.78 313	98.31 325

tpm		97.15 294	96.95 298	97.75 312	98.91 313	94.24 339	99.32 97	97.96 331	97.71 273	98.29 308	99.32 257	86.72 346	99.92 85	98.10 166	96.24 350	99.09 274

E-PMN		97.14 296	97.43 284	96.27 334	98.79 329	91.62 352	95.54 351	99.01 300	99.44 95	98.88 269	99.12 292	92.78 301	99.68 310	94.30 328	99.03 303	97.50 342

cascas		96.99 297	96.82 303	97.48 317	97.57 356	95.64 329	96.43 349	99.56 175	91.75 345	97.13 345	97.61 352	95.58 278	98.63 353	96.68 266	99.11 298	98.18 334

thisisatest0515		96.98 298	96.42 305	98.66 282	99.42 228	97.47 296	97.27 338	94.30 353	97.24 295	99.15 241	98.86 323	85.01 349	99.87 164	97.10 244	99.39 273	98.63 309

EMVS		96.96 299	97.28 287	95.99 337	98.76 333	91.03 355	95.26 352	98.61 315	99.34 108	98.92 265	98.88 322	93.79 292	99.66 319	92.87 337	99.05 301	97.30 346

dp		96.86 300	97.07 294	96.24 335	98.68 337	90.30 359	99.19 139	98.38 326	97.35 291	98.23 313	99.59 181	87.23 339	99.82 239	96.27 286	98.73 321	98.59 312

baseline2		96.83 301	96.28 307	98.46 289	99.09 301	96.91 311	98.83 215	93.87 355	97.23 296	96.23 350	98.36 342	88.12 337	99.90 123	96.68 266	98.14 337	98.57 315

ET-MVSNet_ETH3D		96.78 302	96.07 311	98.91 259	99.26 272	97.92 285	97.70 319	96.05 347	97.96 261	92.37 355	98.43 341	87.06 340	99.90 123	98.27 148	97.56 345	98.91 296

tpm cat1		96.78 302	96.98 297	96.16 336	98.85 321	90.59 358	99.08 176	99.32 261	92.37 344	97.73 338	99.46 227	91.15 316	99.69 299	96.07 291	98.80 312	98.21 331

PCF-MVS	96.03 18	96.73 304	95.86 315	99.33 203	99.44 221	99.16 198	96.87 345	99.44 228	86.58 351	98.95 260	99.40 236	94.38 287	99.88 151	87.93 348	99.80 153	98.95 292
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CostFormer		96.71 305	96.79 304	96.46 333	98.90 314	90.71 357	99.41 78	98.68 311	94.69 339	98.14 319	99.34 255	86.32 348	99.80 260	97.60 210	98.07 339	98.88 298

MVE	92.54 22	96.66 306	96.11 310	98.31 297	99.68 128	97.55 295	97.94 307	95.60 349	99.37 105	90.68 356	98.70 330	96.56 255	98.61 354	86.94 353	99.55 243	98.77 306
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
thres600view7		96.60 307	96.16 309	97.93 306	99.63 140	96.09 324	99.18 140	97.57 337	98.77 189	98.72 286	97.32 355	87.04 341	99.72 287	88.57 346	98.62 324	97.98 338

EPMVS		96.53 308	96.32 306	97.17 325	98.18 349	92.97 345	99.39 82	89.95 358	98.21 245	98.61 293	99.59 181	86.69 347	99.72 287	96.99 248	99.23 296	98.81 304

thres400		96.40 309	95.89 313	97.92 307	99.58 151	96.11 322	99.00 188	97.54 340	98.43 219	98.52 300	96.98 358	86.85 343	99.67 315	87.62 349	98.51 328	97.98 338

thres100view900		96.39 310	96.03 312	97.47 318	99.63 140	95.93 325	99.18 140	97.57 337	98.75 193	98.70 288	97.31 356	87.04 341	99.67 315	87.62 349	98.51 328	96.81 347

tpm2		96.35 311	96.22 308	96.73 329	98.88 320	91.75 351	99.21 133	98.51 319	93.27 342	97.89 329	99.21 281	84.83 350	99.70 293	96.04 292	98.18 336	98.75 307

FPMVS		96.32 312	95.50 320	98.79 275	99.60 146	98.17 269	98.46 261	98.80 307	97.16 298	96.28 347	99.63 150	82.19 353	99.09 350	88.45 347	98.89 311	99.10 271

tfpn200view9		96.30 313	95.89 313	97.53 316	99.58 151	96.11 322	99.00 188	97.54 340	98.43 219	98.52 300	96.98 358	86.85 343	99.67 315	87.62 349	98.51 328	96.81 347

TESTMET0.1,1		96.24 314	95.84 316	97.41 320	98.24 347	93.84 340	97.38 333	95.84 348	98.43 219	97.81 333	98.56 335	79.77 358	99.89 136	97.77 192	98.77 315	98.52 317

test-mter		96.23 315	95.73 318	97.74 313	98.18 349	95.02 334	97.38 333	96.10 344	97.90 263	97.81 333	98.58 332	79.12 359	99.91 103	97.69 205	98.78 313	98.31 325

X-MVStestdata		96.09 316	94.87 324	99.75 55	99.71 110	99.71 63	99.37 88	99.61 141	99.29 114	98.76 283	61.30 361	98.47 145	99.88 151	97.62 207	99.73 188	99.67 64

thres200		96.09 316	95.68 319	97.33 323	99.48 205	96.22 321	98.53 251	97.57 337	98.06 253	98.37 307	96.73 360	86.84 345	99.61 334	86.99 352	98.57 325	96.16 350

DWT-MVSNet_test		96.03 318	95.80 317	96.71 331	98.50 341	91.93 349	99.25 123	97.87 334	95.99 320	96.81 346	97.61 352	81.02 355	99.66 319	97.20 239	97.98 340	98.54 316

gg-mvs-nofinetune		95.87 319	95.17 323	97.97 305	98.19 348	96.95 309	99.69 29	89.23 359	99.89 12	96.24 349	99.94 13	81.19 354	99.51 342	93.99 334	98.20 333	97.44 343

PVSNet_0	95.53 19	95.85 320	95.31 322	97.47 318	98.78 331	93.48 342	95.72 350	99.40 242	96.18 318	97.37 339	97.73 350	95.73 275	99.58 337	95.49 310	81.40 354	99.36 220

tmp_tt		95.75 321	95.42 321	96.76 327	89.90 359	94.42 338	98.86 210	97.87 334	78.01 353	99.30 218	99.69 112	97.70 211	95.89 355	99.29 58	98.14 337	99.95 1

MVS		95.72 322	94.63 326	98.99 249	98.56 339	97.98 284	99.30 104	98.86 303	72.71 355	97.30 340	99.08 296	98.34 162	99.74 283	89.21 345	98.33 331	99.26 238

PAPM		95.61 323	94.71 325	98.31 297	99.12 293	96.63 315	96.66 348	98.46 322	90.77 348	96.25 348	98.68 331	93.01 299	99.69 299	81.60 354	97.86 343	98.62 310

IB-MVS	95.41 20	95.30 324	94.46 327	97.84 309	98.76 333	95.33 332	97.33 336	96.07 346	96.02 319	95.37 353	97.41 354	76.17 361	99.96 34	97.54 213	95.44 352	98.22 330
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
test123		29.31 325	33.05 330	18.08 339	25.93 361	12.24 361	97.53 327	10.93 362	11.78 356	24.21 357	50.08 365	21.04 362	8.60 357	23.51 355	32.43 356	33.39 353

testmvs		28.94 326	33.33 328	15.79 340	26.03 360	9.81 362	96.77 346	15.67 361	11.55 357	23.87 358	50.74 364	19.03 363	8.53 358	23.21 356	33.07 355	29.03 354

cdsmvs_eth3d_5k		24.88 327	33.17 329	0.00 341	0.00 362	0.00 363	0.00 353	99.62 134	0.00 358	0.00 359	99.13 288	99.82 4	0.00 359	0.00 357	0.00 357	0.00 355

pcd_1.5k_mvsjas		16.61 328	22.14 331	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	100.00 1	99.28 41	0.00 359	0.00 357	0.00 357	0.00 355

uanet_test		8.33 329	11.11 332	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	100.00 1	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

sosnet-low-res		8.33 329	11.11 332	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	100.00 1	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

sosnet		8.33 329	11.11 332	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	100.00 1	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

uncertanet		8.33 329	11.11 332	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	100.00 1	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

Regformer		8.33 329	11.11 332	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	100.00 1	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

uanet		8.33 329	11.11 332	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	100.00 1	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

ab-mvs-re		8.26 335	11.02 338	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	99.16 286	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

ZD-MVS					99.43 223	99.61 100		99.43 231	96.38 314	99.11 247	99.07 297	97.86 202	99.92 85	94.04 332	99.49 258

RE-MVS-def			99.13 116		99.54 173	99.74 54	99.26 116	99.62 134	99.16 137	99.52 165	99.64 140	98.57 129		97.27 230	99.61 231	99.54 149

IU-MVS					99.69 120	99.77 40		99.22 284	97.50 283	99.69 102				97.75 194	99.70 199	99.77 32

OPU-MVS				99.29 213	99.12 293	99.44 133	99.20 134				99.40 236	99.00 72	98.84 352	96.54 273	99.60 234	99.58 130

test_241102_TWO								99.54 185	99.13 143	99.76 74	99.63 150	98.32 165	99.92 85	97.85 187	99.69 202	99.75 39

test_241102_ONE					99.69 120	99.82 25		99.54 185	99.12 146	99.82 49	99.49 217	98.91 83	99.52 341

9.14			98.64 206		99.45 218		98.81 220	99.60 152	97.52 282	99.28 219	99.56 194	98.53 138	99.83 229	95.36 315	99.64 221

save fliter					99.53 178	99.25 180	98.29 270	99.38 251	99.07 151

test_0728_THIRD									99.18 132	99.62 129	99.61 168	98.58 128	99.91 103	97.72 196	99.80 153	99.77 32

test_0728_SECOND				99.83 22	99.70 117	99.79 35	99.14 155	99.61 141					99.92 85	97.88 181	99.72 193	99.77 32

test0726					99.69 120	99.80 33	99.24 124	99.57 170	99.16 137	99.73 91	99.65 138	98.35 160

GSMVS																99.14 265

test_part2					99.62 143	99.67 79				99.55 157

sam_mvs1												90.81 323				99.14 265

sam_mvs												90.52 327

ambc				99.20 230	99.35 243	98.53 247	99.17 145	99.46 223		99.67 108	99.80 52	98.46 148	99.70 293	97.92 178	99.70 199	99.38 214

MTGPA								99.53 194

test_post1							99.14 155				51.63 363	89.54 334	99.82 239	96.86 255

test_post											52.41 362	90.25 329	99.86 184

patchmatchnet-post											99.62 159	90.58 325	99.94 54

GG-mvs-BLEND				97.36 321	97.59 354	96.87 312	99.70 23	88.49 360		94.64 354	97.26 357	80.66 356	99.12 349	91.50 341	96.50 349	96.08 351

MTMP							99.09 173	98.59 317

gm-plane-assit					97.59 354	89.02 360			93.47 341		98.30 343		99.84 218	96.38 282

test9_res														95.10 318	99.44 264	99.50 172

TEST9					99.35 243	99.35 161	98.11 286	99.41 235	94.83 338	97.92 327	98.99 306	98.02 189	99.85 202

test_8					99.34 253	99.31 167	98.08 290	99.40 242	94.90 334	97.87 331	98.97 312	98.02 189	99.84 218

agg_prior2														94.58 326	99.46 263	99.50 172

agg_prior					99.35 243	99.36 157		99.39 245		97.76 336			99.85 202

TestCases				99.63 109	99.78 72	99.64 88		99.83 32	98.63 200	99.63 122	99.72 92	98.68 114	99.75 281	96.38 282	99.83 131	99.51 166

test_prior4						99.19 196	98.00 298

test_prior2							97.95 305		97.87 265	98.05 321	99.05 299	97.90 198		95.99 295	99.49 258

test_prior				99.46 166	99.35 243	99.22 189		99.39 245					99.69 299			99.48 181

旧先验2							97.94 307		95.33 329	98.94 261			99.88 151	96.75 262

新几何2							98.04 294

新几何1				99.52 149	99.50 194	99.22 189		99.26 276	95.66 326	98.60 294	99.28 266	97.67 216	99.89 136	95.95 299	99.32 284	99.45 192

旧先验1					99.49 199	99.29 170		99.26 276			99.39 240	97.67 216			99.36 279	99.46 190

无先验							98.01 296	99.23 283	95.83 322				99.85 202	95.79 304		99.44 197

原ACMM2							97.92 309

原ACMM1				99.37 196	99.47 210	98.87 232		99.27 274	96.74 310	98.26 310	99.32 257	97.93 196	99.82 239	95.96 298	99.38 274	99.43 203

test222					99.51 188	99.08 210	97.83 314	99.29 270	95.21 331	98.68 289	99.31 259	97.28 236			99.38 274	99.43 203

testdata2													99.89 136	95.99 295

segment_acmp												98.37 158

testdata				99.42 177	99.51 188	98.93 225		99.30 268	96.20 317	98.87 270	99.40 236	98.33 164	99.89 136	96.29 285	99.28 288	99.44 197

testdata1							97.72 317		97.86 268

test12				99.54 146	99.29 266	99.33 164		99.16 290		98.43 305		97.54 224	99.82 239		99.47 261	99.48 181

plane_prior7					99.58 151	99.38 151

plane_prior6					99.47 210	99.26 176						97.24 237

plane_prior5								99.54 185					99.82 239	95.84 302	99.78 164	99.60 116

plane_prior4											99.25 272

plane_prior3						99.31 167			98.36 228	99.14 243

plane_prior2							98.80 223		98.94 165

plane_prior1					99.51 188

plane_prior						99.24 185	98.42 262		97.87 265						99.71 197

n2								0.00 363

nn								0.00 363

door-mid								99.83 32

lessismore_v0				99.64 105	99.86 30	99.38 151		90.66 357		99.89 27	99.83 41	94.56 286	99.97 17	99.56 24	99.92 73	99.57 136

LGP-MVS_train				99.74 60	99.82 43	99.63 92		99.73 79	97.56 278	99.64 118	99.69 112	99.37 30	99.89 136	96.66 268	99.87 107	99.69 51

test11								99.29 270

door								99.77 60

HQP5-MVS						98.94 221

HQP-NCC					99.31 260		97.98 301		97.45 285	98.15 315

ACMP_Plane					99.31 260		97.98 301		97.45 285	98.15 315

BP-MVS														94.73 322

HQP4-MVS										98.15 315			99.70 293			99.53 154

HQP3-MVS								99.37 252							99.67 212

HQP2-MVS												96.67 253

NP-MVS					99.40 232	99.13 201					98.83 324

MDTV_nov1_ep13_2view						91.44 354	99.14 155		97.37 290	99.21 232		91.78 311		96.75 262		99.03 286

MDTV_nov1_ep13			97.73 278		98.70 336	90.83 356	99.15 153	98.02 330	98.51 213	98.82 275	99.61 168	90.98 318	99.66 319	96.89 254	98.92 308

ACMMP++_ref															99.94 61

ACMMP++															99.79 158

Test By Simon												98.41 152

ITE_SJBPF				99.38 193	99.63 140	99.44 133		99.73 79	98.56 206	99.33 210	99.53 204	98.88 88	99.68 310	96.01 293	99.65 219	99.02 288

DeepMVS_CX				97.98 304	99.69 120	96.95 309		99.26 276	75.51 354	95.74 352	98.28 344	96.47 259	99.62 330	91.23 342	97.89 342	97.38 344

High-resolution multi-view benchmark